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Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200countries and territories, which has resulted in a great public health concernacross the international community. Analysis of X-ray imaging data can play acritical role in timely and accurate screening and fighting against COVID-19.Supervised deep learning has been successfully applied to recognize COVID-19pathology from X-ray imaging datasets. However, it requires a substantialamount of annotated X-ray images to train models, which is often not applicableto data analysis for emerging events such as COVID-19 outbreak, especially inthe early stage of the outbreak. To address this challenge, this paper proposesa two-path semi-supervised deep learning model, ssResNet, based on ResidualNeural Network (ResNet) for COVID-19 image classification, where two pathsrefer to a supervised path and an unsupervised path, respectively. Moreover, wedesign a weighted supervised loss that assigns higher weight for the minorityclasses in the training process to resolve the data imbalance. Experimentalresults on a large-scale of X-ray image dataset COVIDx demonstrate that theproposed model can achieve promising performance even when trained on very fewlabeled training images.

Semi-supervised Learning for COVID-19 Image Classification via ResNet